Conceptual Similarity Promotes Memory Generalization At the Cost of Detailed Recollection
Bibliographic record
Abstract
Abstract A cardinal feature of episodic memory is the ability to generalize knowledge across similar experiences to make inference about novel events. Here, we tested if this ability to apply generalized knowledge exists for experiences that are similar in terms of underlying concepts, prior knowledge, and if this comes at the expense of another feature of episodic memory: forming detailed recollection of events Over three experiments, healthy participants performed a modified version of the acquired equivalence test in which they learned overlapping object-scenes associations (A-X, B-X and A-Y) and then generalized the acquired knowledge to indirectly learned associations (B-Y) and novel objects (C-X and C-Y) that were from the same conceptual category (e.g. A - pencil; B - scissors) and different categories (e.g. A - watch; B - fork). In a subsequent recognition memory task, participants made old/new judgements to old (targets), similar (lures) and novel items. Across all experiments, we found that indirect associations that were rooted in conceptual similarity knowledge led to higher rates of generalisation but reduced detailed object memory. Our findings suggest that activating prior conceptual knowledge emphasizes the generalization function of episodic memory at the expense of detailed recollection. We discuss how this trade-off between generalization and recollection functions of episodic memory result from engaging different representations during learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".